HoloMine: A Synthetic Dataset for Buried Landmines Recognition using Microwave Holographic Imaging

TL;DR

This paper introduces a large synthetic microwave holographic dataset for buried landmine detection, aiming to facilitate research and improve automated recognition methods despite current challenges in performance.

Contribution

The creation of the first synthetic microwave holographic dataset for buried landmine detection, enabling new research avenues in computer vision and remote sensing.

Findings

Deep learning models trained on the dataset show limited performance, highlighting task difficulty.

The dataset demonstrates the potential of holographic radars for high-resolution landmine detection.

Initial results suggest room for improvement in automated landmine recognition methods.

Abstract

The detection and removal of landmines is a complex and risky task that requires advanced remote sensing techniques to reduce the risk for the professionals involved in this task. In this paper, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in landmine detection. The dataset consists of 41,800 microwave holographic images (2D) and their holographic inverted scans (3D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography sensor. We evaluate the performance of several state-of-the-art deep learning models trained on our synthetic dataset for various classification tasks. While the results do not yield yet high performances, showing the difficulty of the proposed task, we believe that our dataset has significant potential to drive progress in the field of landmine detection thanks to the accuracy and resolution obtainable using holographic radars. To the best of our knowledge, our dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.